Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	138,106
2	South Dakota	136,073
3	Rhode Island	135,223
4	Utah	122,034
5	Tennessee	119,066
6	Arizona	116,954
7	Iowa	113,593
8	Oklahoma	112,279
9	Wisconsin	111,345
10	Nebraska	111,301
11	Arkansas	110,234
12	South Carolina	109,722
13	New Jersey	108,473
14	Alabama	106,212
15	Kansas	105,591
16	Indiana	104,760
17	Mississippi	103,526
18	Idaho	103,201
19	Delaware	102,196
20	Illinois	101,994
21	New York	101,349
22	Nevada	100,371
23	Montana	99,884
24	Florida	99,716
25	Georgia	99,309
26	Wyoming	98,706
27	Kentucky	98,141
28	Texas	97,987
29	Minnesota	97,023
30	Louisiana	96,944
31	Missouri	96,763
32	Massachusetts	96,473
33	California	93,875
34	New Mexico	92,700
35	Connecticut	91,801
36	North Carolina	90,131
37	Ohio	89,480
38	Alaska	88,863
39	Pennsylvania	85,415
40	Michigan	85,120
41	Colorado	84,441
42	West Virginia	82,137
43	Virginia	75,005
44	Maryland	71,213
45	New Hampshire	66,178
46	District of Columbia	65,475
47	Washington	50,247
48	Puerto Rico	47,421
49	Maine	41,194
50	Oregon	40,829
51	Vermont	34,436
52	Hawaii	22,065

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Michigan	1,004
2	Rhode Island	677
3	Connecticut	534
4	Pennsylvania	422
5	New Jersey	375
6	Maine	350
7	Alaska	341
8	Delaware	323
9	North Carolina	319
10	Minnesota	298
11	New York	287
12	Colorado	286
13	New Hampshire	276
14	Florida	270
15	Puerto Rico	264
16	Massachusetts	259
17	South Dakota	253
18	Tennessee	247
19	Illinois	245
20	Idaho	218
21	North Dakota	205
22	Washington	200
23	Maryland	199
24	West Virginia	192
25	Ohio	185
26	Virginia	181
27	District of Columbia	179
28	Nebraska	168
29	Montana	163
30	Indiana	162
31	Texas	161
32	New Mexico	160
33	Kentucky	154
34	Vermont	152
35	Wisconsin	149
36	Wyoming	146
37	Iowa	145
38	South Carolina	145
39	Georgia	139
40	Louisiana	131
41	Oregon	129
42	Alabama	127
43	Kansas	126
44	Oklahoma	124
45	Nevada	122
46	Utah	111
47	Missouri	103
48	Arizona	78
49	Mississippi	74
50	California	72
51	Hawaii	46
52	Arkansas	45

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,813
2	New York	2,613
3	Massachusetts	2,528
4	Rhode Island	2,493
5	Mississippi	2,393
6	Arizona	2,350
7	Connecticut	2,239
8	Louisiana	2,207
9	South Dakota	2,201
10	Alabama	2,187
11	Pennsylvania	1,998
12	North Dakota	1,974
13	Indiana	1,958
14	New Mexico	1,906
15	Illinois	1,883
16	Arkansas	1,882
17	Iowa	1,856
18	South Carolina	1,807
19	Georgia	1,789
20	Michigan	1,770
21	Tennessee	1,747
22	Nevada	1,737
23	Texas	1,707
24	Kansas	1,697
25	Oklahoma	1,692
26	Delaware	1,633
27	Ohio	1,618
28	Florida	1,590
29	District of Columbia	1,541
30	West Virginia	1,537
31	California	1,535
32	Missouri	1,482
33	Kentucky	1,428
34	Montana	1,427
35	Maryland	1,404
36	Wisconsin	1,271
37	Minnesota	1,251
38	Virginia	1,231
39	Wyoming	1,214
40	Nebraska	1,203
41	North Carolina	1,177
42	Idaho	1,123
43	Colorado	1,095
44	New Hampshire	927
45	Washington	710
46	Puerto Rico	683
47	Utah	674
48	Oregon	583
49	Maine	563
50	Alaska	410
51	Vermont	379
52	Hawaii	331

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Colorado	5
2	Missouri	5
3	Delaware	4
4	Michigan	4
5	New Jersey	4
6	Connecticut	3
7	District of Columbia	3
8	Georgia	3
9	Louisiana	3
10	New Mexico	3
11	New York	3
12	Pennsylvania	3
13	Vermont	3
14	Arkansas	2
15	California	2
16	Florida	2
17	Idaho	2
18	Kentucky	2
19	Maryland	2
20	Mississippi	2
21	Nevada	2
22	North Carolina	2
23	Ohio	2
24	Oklahoma	2
25	Puerto Rico	2
26	West Virginia	2
27	Alabama	1
28	Arizona	1
29	Illinois	1
30	Indiana	1
31	Maine	1
32	Massachusetts	1
33	Minnesota	1
34	North Dakota	1
35	Rhode Island	1
36	South Carolina	1
37	Tennessee	1
38	Texas	1
39	Virginia	1
40	Washington	1
41	Wisconsin	1
42	Wyoming	1
43	Alaska	0
44	Hawaii	0
45	Iowa	0
46	Kansas	0
47	Montana	0
48	Nebraska	0
49	New Hampshire	0
50	Oregon	0
51	South Dakota	0
52	Utah	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	355,387	1	99
Chattahoochee	Georgia	345,925	2	99
Bent	Colorado	266,631	3	99
Dewey	South Dakota	245,418	4	99
Lincoln	Arkansas	244,856	5	99
Davidson	Tennessee	138,320	195	93
Richland	South Carolina	107,858	1014	67
York	South Carolina	104,994	1143	63
Orange	California	84,555	2097	33
Pierce	Washington	49,956	2895	7

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Foard	Texas	8,658	1	99
Gove	Kansas	8,346	2	99
Jerauld	South Dakota	7,948	3	99
Galax city	Virginia	7,878	4	99
Emporia city	Virginia	7,856	5	99
Orange	California	1,531	1787	43
Davidson	Tennessee	1,328	2024	35
York	South Carolina	1,313	2043	34
Richland	South Carolina	1,292	2075	33
Pierce	Washington	693	2722	13

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.